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A self-driving vehicle can navigate autonomously in smart cities without the need for human intervention. The emergence of Autonomous Connected Vehicles (ACVs) poses a substantial threat to public and passenger safety due to the possibility of cyber-attacks, which encompass remote hacking, manipulation of sensor data, and probable disablement or accidents. The sensors collect data to facilitate the network's recognition of local landmarks, such as trees, curbs, pedestrians, signs, and traffic lights. ACVs gather vast amounts of data, encompassing the exact geographical coordinates of the vehicle, captured images, and signals received from various sensors. To create a fully autonomous system, it is imperative to intelligently integrate several technologies, such as sensors, communication, computation, machine learning (ML), data analytics, and other technologies. The primary issues in ACVs involve data privacy and security when instantaneously exchanging substantial volumes of data. This study investigates related data security and privacy research in ACVs using the Blockchain-enabled Federated Reinforcement Learning (BFRL) framework. This paper provides a literature review examining data security and privacy in ACVs and the BFRL framework that can be used to protect ACVs. This study presents the integration of FRL and Blockchain (BC) in the context of smart cities. Furthermore, the challenges and opportunities for future research on ACVs utilising BFRL frameworks are discussed. [ABSTRACT FROM AUTHOR]

To execute seamless computing along with information sharing, Cloud Computing (CC) plays an important role in measurable resource sharing. But, owing to its open and distributed framework, privacy is a major risk as it is open to attackers. To accomplish their malicious goals, cloud services are abused by intruders. In this paper, using Linear Sigmoid Singleton Deep Neural Network (LS2DNN)with Pearson correlation and Brownian motion induced K-Anonymity (PBKA)centered lightweight privacy preservation technique in the cloud server, an efficient federated learning-based Intrusion Detection System (IDS) is proposed. From the publically available sources, NSL-KDD datasets are downloaded; also, Feature Extraction (FE), null feature removal, and feature mapping are executed. Subsequently, for feature selection, Chebyshev Chaotic Mapping adapted Jaya Optimization Algorithm (C2MJOA) is wielded. Then, for classifying attacked and normal data, the LS2DNNclassifier is utilized.The incoming data from the data owners are privacy preserved via federated learning-centered Pearson correlation and Brownian motion-induced K-Anonymity (PBKA) while testing; moreover, the data is verified for intrusion. Finally, when compared with the prevailing models, the proposed model attained better results. [ABSTRACT FROM AUTHOR]

The convergence from the electric grid to the smart microgrid motivates the incorporation of the intrusion detection system to identify intruders and mitigate the resultant damages to ensure system stability. It is planned to employ the Deep Belief Network (DBN), which is one of the deep learning techniques with some improvement to detect the attacks in a microgrid. To improve the accuracy of the detection, a rule-based detection technique is added to enhance the detection of intruders using DBN. The proposed technique is supported with the layered micro-grid architecture that makes the system flexible and simple toward the implementation. The proposed Enhanced DBN (EDBN) performance is measured in different bus representations for identifying the higher hit rate and rejection rate, lesser miss rate and false-positive rate. Two attacks, such as False Data Injection and Denial of Service attacks, are generated by Greedy Algorithm and are detected by the proposed technique. Compared to the existing detection and control system, the proposed EDBN technique provides accuracy higher than 92%, false alarm rate less than 1%. Thus, the experimental results show that the proposed technique accuracy is higher than the existing intrusion detection techniques in a microgrid. [ABSTRACT FROM AUTHOR]

A novel organic acid-modified starch and carboxymethyl cellulose (CMC) based films plasticized with glycerol were prepared from unconventional tikhur starch (Curcuma angustifolia) by solution casting. Wet milling was used in the laboratory to extract starch from the tikhur rhizome. Carboxymethyl cellulose, at a concentration of (0.2 g−1 starch dry basis) was blended with the starch to improve its film-forming properties. Three different treatments with varying organic acids (lactic, citric, and acetic acid) with a concentration of 5% w/w of starch (2 ppm) in a film-forming solution were given. The effect of organic acid incorporation on the antibacterial, morphological, structural, thermal, and crystalline properties of developed films was studied. The minimum inhibitory concentration values of the three organic acids against gram-negative (E. coli) and gram-positive (S. aureus) bacteria were measured using the tube dilution method. The MIC results revealed that lactic acid and citric acid are effective against both gram-negative and gram-positive bacteria, while acetic acid showed more effectiveness against gram-negative bacteria (E. coli). MBC results revealed that organic acids have potent bactericidal activity. Citric acid resulted in higher inhibition for gram-positive bacteria (S. aureus) compared to gram-negative bacteria (E. coli.). While acetic acid showed higher inhibition for E. coli. than S. aureus. Lactic acid displayed similar inhibition against both S. aureus and E. coli. Among different organic acids, lactic acid incorporation resulted in a more homogeneous, transparent, and thermally stable film. As evidenced by the micrographs, the lactic acid incorporation resulted in a compact film structure without any visible cracks. While X-ray diffraction showed an increase in crystalline properties due to organic acid modification. In this study, it was indicated that modification with organic acids (polycarboxylic acids) effectively improved the overall properties of developed films depending on the type of organic acid used. The developed films have the potential to replace harmful synthetic films in food packaging. [ABSTRACT FROM AUTHOR]

Advances in technology always had an impact on our lives. Several emerging technologies, most notably the Internet of Things (IoT) and blockchain, present transformative opportunities. The blockchain is a decentralized, transparent ledger for storing transaction data. By effectively establishing trust between nodes, it has the remarkable potential to design unique architectures for most enterprise applications. When it first appeared as a platform for anonymous cryptocurrency trading, such as Bitcoin, on a public network platform, blockchain piqued the interest of researchers. The chain is completed when each block connects to the previous block. The Internet of Things (IoT) is a network of networked devices that can exchange data and be managed and controlled via unique identifiers. Automation, wireless sensor networks, embedded systems, and control systems are just a few of the well-known technologies that power the IoT. Converging advancements in real-time analytics, machine learning, commodity sensors, and embedded systems demonstrate the rapid expansion of the IoT paradigm. The Internet of Things refers to the global networking of millions of networked smart gadgets that gather and exchange data. Integrating the IoT and blockchain technology would be a significant step toward developing a reliable, secure, and comprehensive method of storing data collected by smart devices. Internet-enabled devices in the IoT can send data to private blockchain networks, creating immutable records of all transaction history. As a result, these networks produce unchangeable logs of all transactions. This research looks at how blockchain technology and the Internet of Things interact to understand better how devices can communicate with one another. The blockchain-enabled Internet of Things architecture proposed in this article is a useful framework for integrating blockchain technology and the Internet of Things using the most cutting-edge tools and methods currently available. This article discusses the principles of blockchain-based IoT, consensus methods, reviews, difficulties, prospects, applications, trends, and communication between IoT nodes in an integrated framework. [ABSTRACT FROM AUTHOR]

Educational development processes are essential for successful academic performance in educational and technical environments. Teachers and students also need a model and guidelines required for effective learning. Without effective curriculum mapping, the institutions cannot accurately estimate outcomes and maximize potential performance on resources. A matrix depicts the relationship between student learning outcomes (SOs) and topics on the curricular map. The need to earn satisfying produce of education and achieve considerable progress in the visibility of education equity in completing professional duties is a primary motivation for learning the curriculum. One of the most effective strategies to increase overall teaching effectiveness, involvement, or curricular interaction is curriculum development. The mapping connects all disciplines to academic outcomes and displays well-planned teaches. An excellent example of a curriculum should be well-prepared and purposefully encourage expertise acquisition. This paper describes a set of range standards and recommendations for this technique and challenges that affect curricular map construction. As a result, this strategy will increase the overall performance of education and the quality of the curriculum. [ABSTRACT FROM AUTHOR]